While Tsipras and his Syriza coalition have been busy in Greek parliament, the Internet has been a-buzz with speculation that their platform will result in a Greek exit from the Euro currency. This prospect, affectionately dubbed "Grexit" by Citi in February, has been making the rounds on Twitter under the hashtag #grexit. We think the
The NATO summit is currently being held in Chicago, and, as is typical for NATO or G# summits, the streets and tweets are full of dissent. In the spirit of my past investigations of online dissent (#jan25, #25bahman, #12fev, #wiunion, #cn220, #march15), I thought I would investigate the #nonato tag, where Twitter users around
Let's say you've used my Python script to automate the download of a hashtag or search phrase from Twitter (in a Unicode safe way, unlike within R). Now let's say you want to visualize the number of tweets over time. Easy enough - I've also shared this R/ggplot2 code that accomplishes the task. However, let's say
Last year, I covered a number of the so-called "Twitter protests" in China (#cn220), Iran (#25bahman), and Algeria (#fev12). Since these protests began in January 2011, the Arab Spring has claimed many members of both ruling and revolting groups - Mubarak in Egypt, Gaddafi in Libya, Ben Ali in Tunisia, Saleh in Yemen, and
Well, so much for that #march11 #Saudi day of rage. Whether it was really the "tempest in a teacup" that Prince Al-Waleed suggested on CNBC (video below, transcript here) or not, the oil complex and Saudi markets seem to have shrugged off much of the risk that was priced in after Thursday's rumors of shots.
Last week, I posted some R code that downloads the user and timestamp of tweets that contain a given hashtag going back as far as Twitter search will allow. As I noted in the post, the text of these tweets isn’t stored because of encoding issues with R and its JSON packages. A few people emailed asking
I've been playing with Twitter data over the last week, archiving Algerian, Egyptian, Iranian, and Chinese tweets. I thought I'd bring the story a little closer to home this time by archiving tweets from Wisconsin Union protesters on the #wiunion tag. Grab the dataset of 165,593 tweets here, and check out the two figure
Out of all the visuals I’ve produced, I think the "coolest" is the three-dimensional U.S. Supreme Court citation network 1080p movie I produced with Dan Katz (close friend, coauthor, and newly minted law professor!). 3D networks, especially dynamic ones, really invoke the "wow" factor. Movies are especially important in dynamic cases too, since without the animation,
I’ve posted three examples of Twitter hashtags datasets in the last week: one on China, one on Iran, and one on Algeria. In order to build these datasets, I needed to obtain older tweets; this is slightly more difficult than simply filtering the streaming feed for your hashtag of choice. The original code I wrote
Earlier this week, I posted a ~100k tweet dataset on the #25bahman protests in Iran. The corresponding figure of frequencies showed a strong presence on Twitter, with over 500 tweets per 5 minute period at peak. You can download the dataset or check out the figure in that post. I decided to take a quick
